Jira Expert in Berlin

• Overall leadership of several strategic operations projects focusing on Workplace Services, SLA Framework, access management, certificate management, e-learning, backup & recovery, test management, and capacity management, as well as the introduction of system monitoring and feasibility studies for 24x7 operations, in some cases including implementation in ServiceNow
• Creation of various ServiceNow operating concepts covering training, access rights, and emergency management as part of a new cloud hosting initiative for the ServiceNow platform
• Executive board reports and leadership of steering committees as part of company-wide strategic objectives, as well as the establishment of new balanced scorecards for measuring KPIs related to optimization-driven project results

Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.

• Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
• Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
• Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
• Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
• Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
• Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.

Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.

In-house consulting provider and driver of transformation within the group, multi-stakeholder environment and C-level.

Realignment and stabilization of a cross-functional transformation and scaling program within the group. Sharpening the target picture, priorities, and set of measures, as well as building reliable governance, planning, and steering structures. Structuring roles, responsibilities, and strategic initiatives while including AI and IT automation ideas.

• Designed program realignment and project portfolio management

• Developed strategy model and target picture for IT projects

• Structured portfolio, roadmap, and priorities

• Established governance and regular meetings

• Worked out operating model for flagship projects

• Assessed AI and automation ideas

• Clarified roles and responsibilities

• Implemented change measures

• Developed, moderated, and evaluated workshops

• Transformed 17 initiatives into a steering model

• Increased transparency and decision-making ability

• Strengthened commitment in steering

• Sharpened the operating model structurally

• Integrated three top-5 institutes

• Involved over 80% of stakeholders

• Governance

• Portfolio steering (PPM)

• Change management

• Artificial intelligence

• Workflow automation

• AI use case assessment

• Confluence

• Jira

• Stakeholder management

• LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)

• Agentic AI pipeline for automated Scope 3 emissions calculations with 150.000+ validated data records

• Intelligent API workflows for real-time carbon footprint calculations in ERP and ESG systems

• ML algorithms for predicting emissions hotspots and optimizing product design

• Automated data validation pipelines with NLP for quality assurance of CO₂e datasets

• Led a 15-person cross-functional team in developing 10+ AI features

• Strategic product planning and AI roadmap with 35% shorter time-to-market

• Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)

• On-time project delivery with 95% budget adherence through data-driven backlog management

• Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% increase in team velocity)

• Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)

• WiNOWiG enhancements (digitalization & organization)
• Project management for the WiNOWiG project (regulatory offenses)
• Process optimization and coordination

• Building up the Enterprise Architecture department. Preparing the convergence of IT / OT.
• Mapping the global business application landscape (6 countries).
• Concept for a graph model of the application landscape. Storing the results in the open-source graph database Neo4J. Various reports in the database query language CYPHER and the visualization component BLOOM. Automatic generation of IT architecture diagrams.
• Development of an application taxonomy to identify redundant applications.
• Applying Gartner's Software Portfolio Management TIME model (Tolerate, Invest, Migrate, and Eliminate) for portfolio management.
• Enterprise Architecture Governance: establishing architecture guidelines for documenting measures and decisions.
• Process modeling according to Porter (Value Chain).
• Various architecture approvals for change requests and documentation in Architecture Decision Records (ADR).
• Working on the RACI matrix for various processes.
• Working on business capabilities as preparation for a LeanIX rollout.
• Contributing to the concept for IT/OT convergence.
• Establishing architecture guidelines in a very pragmatic corporate culture. Knowledge about applications and responsibilities is spread across the world.

• Shipped Recare Voice Desktop from 0 to 1 in two months, including multi-language clinical documentation that auto-transcribes into structured German medical notes.
• Reduced LLM inference costs by 60–70% across Docs and Extract through prompt caching architecture.
• Built the AI workbench used by PMs/engineers for prompt experimentation and the Langfuse eval stack (10k+ traces evaluated).

Workflow-automation SaaS for operations teams (Berlin, 120 people); full-time freelance engagement reporting to the CEO: an initial 12-month interim mandate, extended twice through the AI build-out; owned product for one squad and coached the other product managers on process.

• Led generative AI (LLM) integration into the core product: from LLM-powered steps to natural-language workflow authoring and step-level automation suggestions, plus AI-managed dynamic workflows, shipped behind eval gates with human-in-the-loop fallbacks: AI-drafted workflows grew to 31% of all new workflows, and median time-to-first-workflow fell from 3 days to 4 hours.
• Packaged the AI capabilities as a usage-based add-on priced on executed automation steps, working with sales and marketing on positioning: ~€800K added ARR in the first year, and adopting accounts churned 1.8 pp less.
• Owned the roadmap end to end: replaced feature-request-driven quarterly planning with an outcome-based rolling roadmap built on quarterly bets and explicit kill criteria, presented monthly to the executive team and quarterly to the board.
• Rebuilt the product-management operating system: weekly customer-discovery cadence incl. workshop facilitation, RFC/decision-doc reviews and a single quarterly metrics narrative; coached four product managers, one promoted to senior during the engagement.
• Closed the engagement as scoped: hired and onboarded the permanent VP Product, handed over the process playbook and roadmap, and exited on schedule in June 2026.

• PROBLEM: GTM effort is guesswork across fragmented identities and channels, with no closed feedback loop.
• BUILT: Cost-pyramid engine (L0–L3): identity resolution across ~25k entities, explainable intent scoring, and a closed decision loop (propose → execute → evaluate → learn) with calibration.
• IMPACT: Shipped v1.3.1 with a live demo; 99% of operations resolve at the free L0 tier (CI-enforced); $0 to run without any API key.

Driving AI model quality at scale — evaluating prompt-response accuracy, flagging edge cases, and maintaining SLA-compliant workflows across distributed global teams.

• Analyse AI prompts and side-by-side model outputs to assess response quality, factual accuracy, relevance, consistency, and compliance with project evaluation guidelines.
• Perform fact-checking, data validation, troubleshooting, issue identification, and edge-case review to improve quality standards across AI training support workflows.
• Use Google Sheets, Google Docs, and browser-based tools to document findings, maintain evaluation logs, track issue patterns, and support workflow optimisation in a remote environment.
• Create clear written justifications, review summaries, and KPI-oriented reporting focused on accuracy, turnaround time, documentation completeness, defect identification rate, and SLA adherence.

• As UX Manager and UX Team Lead, my focus is on creating the conditions for excellent UX design – not only by improving results, but also by investing in people, culture, and systems. I am convinced that sustainable UX design comes from a combination of autonomous teams, clear rituals, and a shared vision of product and user value.

• My job is to enable successful UX design within a company. This includes setting up and growing the UX team, establishing a design system, designing design processes, defining and developing UX maturity and UX culture within the company, and developing strategies that combine business requirements and user experience design.

• Consulting and conceptual support in UX and product topics (project-based)

• Deepening strategic topics: UX Strategy, Organizational Design, UX Maturity

• Market and role orientation with a focus on Head of UX / UX Leadership positions

• Lead Product Owner role for the BFSG pilot journey “Search and Book” across web and app, focusing on digital accessibility, UX, conversion, and regulatory implementation.
• Created and prioritized the product backlog; derived user stories and acceptance criteria from legal standards (BFSG, WCAG 2.1)
• Developed a product vision that combines digital accessibility, user-centered UX, and conversion optimization for the booking journey
• Cross-stream release management and quality assurance across the entire web and app area, including go/no-go decisions, rollout, and user acceptance tests
• Coordinated and prioritized with Legal, Compliance, external BFSG consultants, UX, Development, Marketing, and Management
• Conducted user research as well as usability and accessibility tests (including screen readers and keyboard navigation) and prototyping with UX/UI
• Defined KPIs to measure accessibility, conversion, and user guidance
• BFSG pilot journey “Search and Book” successfully delivered: first accessible features rolled out and the foundation for a long-term BFSG-compliant booking journey established
• Established sustainable process integration through close collaboration with consulting partners and internal teams

• Designing UX for a B2B AI SaaS platform covering the full software development lifecycle, including an orchestration transparency panel showing users which AI model is active at each stage, reducing AI opacity and building user trust in multi-model workflows.

• Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
• Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
• Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
• Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
• Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
• Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
• Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.